A Multi-Objective Mission Planning Method for AUV Target Search

How an autonomous underwater vehicle (AUV) performs fully automated task allocation and achieves satisfactory mission planning effects during the search for potential threats deployed in an underwater space is the focus of the paper. First, the task assignment problem is defined as a traveling salesman problem (TSP) with specific and distinct starting and ending points. Two competitive and non-commensurable optimization goals, the total sailing distance and the turning angle generated by an AUV to completely traverse threat points in the planned order, are taken into account. The maneuverability limitations of an AUV, namely, minimum radius of a turn and speed, are also introduced as constraints. Then, an improved ant colony optimization (ACO) algorithm based on fuzzy logic and a dynamic pheromone volatilization rule is developed to solve the TSP. With the help of the fuzzy set, the ants that have moved along better paths are screened and the pheromone update is performed only on preferred paths so as to enhance pathfinding guidance in the early stage of the ACO algorithm. By using the dynamic pheromone volatilization rule, more volatile pheromones on preferred paths are produced as the number of iterations of the ACO algorithm increases, thus providing an effective way for the algorithm to escape from a local minimum in the later stage. Finally, comparative simulations are presented to illustrate the effectiveness and advantages of the proposed algorithm and the influence of critical parameters is also analyzed and demonstrated.National Natural Science Foundation of China (NSFC) 52101347Foundations for young scientists' cultivation 7900000

Iterated local search using an add and delete hyper- heuristic for university course timetabling

Hyper-heuristics are (meta-)heuristics that operate at a higher level to choose or generate a set of low-level (meta-)heuristics in an attempt of solve difficult optimization problems. Iterated local search (ILS) is a well-known approach for discrete optimization, combining perturbation and hill-climbing within an iterative framework. In this study, we introduce an ILS approach, strengthened by a hyper-heuristic which generates heuristics based on a fixed number of add and delete operations. The performance of the proposed hyper-heuristic is tested across two different problem domains using real world benchmark of course timetabling instances from the second International Timetabling Competition Tracks 2 and 3. The results show that mixing add and delete operations within an ILS framework yields an effective hyper-heuristic approach

Solving Many-Objective Car Sequencing Problems on Two-Sided Assembly Lines Using an Adaptive Differential Evolutionary Algorithm

The car sequencing problem (CSP) is addressed in this paper. The original environment of the CSP is modified to reflect real practices in the automotive industry by replacing the use of single-sided straight assembly lines with two-sided assembly lines. As a result, the problem becomes more complex caused by many additional constraints to be considered. Six objectives (i.e. many objectives) are optimised simultaneously including minimising the number of colour changes, minimising utility work, minimising total idle time, minimising the total number of ratio constraint violations and minimising total production rate variation. The algorithm namely adaptive multi-objective evolutionary algorithm based on decomposition hybridised with differential evolution algorithm (AMOEA/D-DE) is developed to tackle this problem. The performances in Pareto sense of AMOEA/D-DE are compared with COIN-E, MODE, MODE/D and MOEA/D. The results indicate that AMOEA/D-DE outperforms the others in terms of convergence-related metrics

Optimisation sous contraintes de problèmes distribués par auto-organisation coopérative

Quotidiennement, divers problèmes d'optimisation : minimiser un coût de production, optimiser le parcours d'un véhicule, etc sont à résoudre. Ces problèmes se caractérisent par un degré élevé de complexité dû à l'hétérogénéité et la diversité des acteurs en jeu, à la masse importante des données ainsi qu'à la dynamique des environnements dans lesquels ils sont plongés. Face à la complexité croissante de ces applications, les approches de résolution classiques ont montré leurs limites. Depuis quelques années, la communauté scientifique s'intéresse aux développements de nouvelles solutions basées sur la distribution du calcul et la décentralisation du contrôle plus adaptées à ce genre de problème. La théorie des AMAS (Adaptive Multi-Agents Systems) propose le développement de solutions utilisant des systèmes multi-agents auto-adaptatifs par auto-organisation coopérative. Cette théorie a montré son adéquation pour la résolution de problèmes complexes et dynamiques, mais son application reste à un niveau d'abstraction assez élevé. L'objectif de ce travail est de spécialiser cette théorie pour la résolution de ce genre de problèmes. Ainsi, son utilisation en sera facilitée. Pour cela, le modèle d'agents AMAS4Opt avec des comportements et des interactions coopératifs et locaux a été défini. La validation s'est effectuée sur deux problèmes clés d'optimisation : le contrôle manufacturier et la conception de produit complexe. De plus, afin de montrer la robustesse et l'adéquation des solutions développées, un ensemble de critères d'évaluation permettant de souligner les points forts et faibles des systèmes adaptatifs et de les comparer à des systèmes existants a été défini.We solve problems and make decisions all day long. Some problems and decisions are very challenging: What is the best itinerary to deliver orders given the weather, the traffic and the hour? How to improve product manufacturing performances? etc. Problems that are characterized by a high level of complexity due to the heterogeneity and diversity of the participating actors, to the increasing volume of manipulated data and to the dynamics of the applications environments. Classical solving approaches have shown their limits to cope with this growing complexity. For the last several years, the scientific community has been interested in the development of new solutions based on computation distribution and control decentralization. The AMAS (Adaptive Multi-Agent-Systems) theory proposes to build solutions based on self-adaptive multi-agent systems using cooperative self-organization. This theory has shown its adequacy to solve different complex and dynamic problems, but remains at a high abstraction level. This work proposes a specialization of this theory for complex optimization problem solving under constraints. Thus, the usage of this theory is made accessible to different non-AMAS experts' engineers. Thus, the AMAS4Opt agent model with cooperative, local and generic behaviours and interactions has been defined.This model is validated on two well-known optimization problems: scheduling in manufacturing control and complex product design. Finally, in order to show the robustness and adequacy of the developed solutions, a set of evaluation criteria is proposed to underline the advantages and limits of adaptive systems and to compare them with already existing systems

Ant Colony Optimisation – A Proposed Solution Framework for the Capacitated Facility Location Problem

This thesis is a critical investigation into the development, application and evaluation of ant colony optimisation metaheuristics, with a view to solving a class of capacitated facility location problems. The study is comprised of three phases. The first sets the scene and motivation for research, which includes; key concepts of ant colony optimisation, a review of published academic materials and a research philosophy which provides a justification for a deductive empirical mode of study. This phase reveals that published results for existing facility location metaheuristics are often ambiguous or incomplete and there is no clear evidence of a dominant method. This clearly represents a gap in the current knowledge base and provides a rationale for a study that will contribute to existing knowledge, by determining if ant colony optimisation is a suitable solution technique for solving capacitated facility location problems. The second phase is concerned with the research, development and application of a variety of ant colony optimisation algorithms. Solution methods presented include combinations of approximate and exact techniques. The study identifies a previously untried ant hybrid scheme, which incorporates an exact method within it, as the most promising of techniques that were tested. Also a novel local search initialisation which relies on memory is presented. These hybridisations successfully solve all of the capacitated facility location test problems available in the OR-Library. The third phase of this study conducts an extensive series of run-time analyses, to determine the prowess of the derived ant colony optimisation algorithms against a contemporary cross-entropy technique. This type of analysis for measuring metaheuristic performance for the capacitated facility location problem is not evident within published materials. Analyses of empirical run-time distributions reveal that ant colony optimisation is superior to its contemporary opponent. All three phases of this thesis provide their own individual contributions to existing knowledge bases: the production of a series of run-time distributions will be a valuable resource for future researchers; results demonstrate that hybridisation of metaheuristics with exact solution methods is an area not to be ignored; the hybrid methods employed in this study ten years ago would have been impractical or infeasible; ant colony optimisation is shown to be a very flexible metaheuristic that can easily be adapted to solving mixed integer problems using hybridisation techniques

Management, Technology and Learning for Individuals, Organisations and Society in Turbulent Environments

This book presents the collection of fifty papers which were presented in the Second International Conference on BUSINESS SUSTAINABILITY 2011 - Management, Technology and Learning for Individuals, Organisations and Society in Turbulent Environments , held in Póvoa de Varzim, Portugal, from 22ndto 24thof June, 2011.The main motive of the meeting was growing awareness of the importance of the sustainability issue. This importance had emerged from the growing uncertainty of the market behaviour that leads to the characterization of the market, i.e. environment, as turbulent. Actually, the characterization of the environment as uncertain and turbulent reflects the fact that the traditional technocratic and/or socio-technical approaches cannot effectively and efficiently lead with the present situation. In other words, the rise of the sustainability issue means the quest for new instruments to deal with uncertainty and/or turbulence. The sustainability issue has a complex nature and solutions are sought in a wide range of domains and instruments to achieve and manage it. The domains range from environmental sustainability (referring to natural environment) through organisational and business sustainability towards social sustainability. Concerning the instruments for sustainability, they range from traditional engineering and management methodologies towards “soft” instruments such as knowledge, learning, and creativity. The papers in this book address virtually whole sustainability problems space in a greater or lesser extent. However, although the uncertainty and/or turbulence, or in other words the dynamic properties, come from coupling of management, technology, learning, individuals, organisations and society, meaning that everything is at the same time effect and cause, we wanted to put the emphasis on business with the intention to address primarily companies and their businesses. Due to this reason, the main title of the book is “Business Sustainability 2.0” but with the approach of coupling Management, Technology and Learning for individuals, organisations and society in Turbulent Environments. Also, the notation“2.0” is to promote the publication as a step further from our previous publication – “Business Sustainability I” – as would be for a new version of software. Concerning the Second International Conference on BUSINESS SUSTAINABILITY, its particularity was that it had served primarily as a learning environment in which the papers published in this book were the ground for further individual and collective growth in understanding and perception of sustainability and capacity for building new instruments for business sustainability. In that respect, the methodology of the conference work was basically dialogical, meaning promoting dialog on the papers, but also including formal paper presentations. In this way, the conference presented a rich space for satisfying different authors’ and participants’ needs. Additionally, promoting the widest and global learning environment and participation, in accordance with the Conference's assumed mission to promote Proactive Generative Collaborative Learning, the Conference Organisation shares/puts open to the community the papers presented in this book, as well as the papers presented on the previous Conference(s). These papers can be accessed from the conference webpage (http://labve.dps.uminho.pt/bs11). In these terms, this book could also be understood as a complementary instrument to the Conference authors’ and participants’, but also to the wider readerships’ interested in the sustainability issues. The book brought together 107 authors from 11 countries, namely from Australia, Belgium, Brazil, Canada, France, Germany, Italy, Portugal, Serbia, Switzerland, and United States of America. The authors “ranged” from senior and renowned scientists to young researchers providing a rich and learning environment. At the end, the editors hope, and would like, that this book to be useful, meeting the expectation of the authors and wider readership and serving for enhancing the individual and collective learning, and to incentive further scientific development and creation of new papers. Also, the editors would use this opportunity to announce the intention to continue with new editions of the conference and subsequent editions of accompanying books on the subject of BUSINESS SUSTAINABILITY, the third of which is planned for year 2013.info:eu-repo/semantics/publishedVersio

Self-tuning of game scenarios through self-adaptative multi-agent systems

Les jeux vidéo modernes deviennent de plus en plus complexes, tant par le nombre de règles qui les composent, que par le nombre d'entités artificielles qui y interagissent. D'un point de vue purement ludique, mais également en ayant des ambitions pédagogiques, les jeux doivent proposer aux joueurs des expériences qui correspondent à leurs niveaux de compétences et à leurs capacités. La diversité au sein de la population de joueurs rend difficile, voire impossible, de proposer une expérience qui aille à tout un chacun. Différents niveaux et différentes capacités de progression font que différents joueurs ont des besoins distincts. L'adaptation des jeux est proposée comme une solution pour palier ces difficultés. Cette thèse propose un ensemble de concepts afin que des concepteurs de jeux, ou des experts de différents domaines, puissent exprimer des objectifs pédagogiques ou ludiques, ainsi que des contraintes sur les expériences de jeu. La généricité de ces concepts les rend compatible avec une grande varieté d'application, potentiellement hors du domaine du jeu vidéo. Conjointement à ces concepts, nous proposons un système multi-agent conçu pour modifier dynamiquement les paramètres d'un moteur de jeu, afin que celui-ci satisfasse les objectifs définis par les experts ou les concepteurs. Le système est composé d'un ensemble d'agents autonomes, qui représentent les concepts du domaine. Ils n'ont qu'une vue locale de leur environnement et ne connaissent pas la fonction globale du système. Ils ne cherchent qu'a résoudre coopérativement les problème locaux qu'ils rencontrent. De l'organisation des agents émerge la fonctionnalité du système : l'adaptation de l'expérience de jeu menant à la satisfaction des objectifs ainsi qu'au respect des contraintes. Nous avons conduit plusieurs expériences pour démontrer que le système passe l'échelle, et qu'il est résistant au bruit. Le paradigme avec lequel les objectifs doivent être définis est utilisé dans des contextes variés pour démontrer sa généricité. D'autres applications démontrent que le système est capable d'adapter une expérience du joueur même quand les conditions de jeu évoluent significativement au cours du temps.Modern video games are getting more and more complex, by exhibiting more and more rules, as well as a growing number of co-existing artificial entities. Whether they only have entertainment objectives, or pedagogical ambitions, they need to provide a game experience that matches the skills and abilities of players. The diversity among the player population makes it difficult, if not impossible, to propose a single game that may suit everyone needs. Different skills, preferences, and progression abilities make players need different game experiences at different times. Adaptation of the game experience is advocated as a solution to keep it adequate. This thesis proposes a set a simple concepts in order for domain experts, games designers or others to express pedagogical or entertainment related objectives, as well as constraints on game experiences. By using only elementary concepts, such as measures and parameters, we remain compliant with a large diversity of domains, even outside of the field of video game. Along with the expression of game requirements, we propose a multi-agent system designed to dynamically modify the various parameters of a game engine, so that the game experience satisfies objectives expressed by experts or designers. The system is composed of a set of autonomous agents representing the domain concepts, that only have a local perception of their environment. They are not aware of the global function of the system, and they only seek to cooperatively solve their local problems. From the organization of these agents, the functionality of the system as a whole emerges: dynamic adaptation of a game experience to satisfy objectives and constraints. We conducted several experiments to demonstrate that the proposed system is scalable and noise resilient. The introduced paradigm with which the requirements must be expressed is used in various context to demonstrate its versatility. Other experiments demonstrate that this system is able to effectively adapt the game experience even when the conditions in which the game takes place significantly change over time

Applying Genetic Algorithms for Software Design and Project Planning

Today's software systems are growing in size and complexity. This means not only increased complexity in developing software systems, but also increase in the budget and completion time. This trend will lead to a situation where traditional manual software engineering practices are not sufficient to develop and evolve software systems in an economic and timely manner. Automated support can aid software engineers in reducing the time-to-market and improving the quality of the software. This thesis work explores the application of genetic algorithms for automated software architecture design and project planning.Software architecture design and project planning are non-trivial and challenging tasks. This thesis applies genetic algorithms to introduce automation into these tasks. The proposed genetic algorithm exploits reusable solutions, such as design patterns, architecture styles and application specific solutions for transforming a given initial rudimentary model into detailed design. The architectures are evaluated using multiple quality attributes, such as modifiability, efficiency and complexity. The fitness function encompasses the knowledge required for evaluating the architectures according to multiple quality attributes. The output from the genetic algorithm is an architecture proposal optimized with respect to multiple quality attributes.A genetic algorithm has also been devised for assigning work across teams located in distributed sites. The genetic algorithm takes information about the target system and the development organization as input and produces a set of work distribution and schedule plans optimized with respect to cost and duration objectives. The fitness function considers the differences in teams and barriers created by global dispersion into account in evaluating the work assignment. In addition, the genetic algorithm also takes solutions that ease or hamper distributed development into account in allocating the work. The genetic algorithm has been further extended with Pareto optimality to find a set of suitable work distribution proposals in a tradeoff between project cost and duration. In the experiments, an electronic home control system was developed by a set of different organizations structures. The results demonstrate that the proposed genetic algorithm can create reasonable work distribution proposals that conform to the general assumptions about the nature of cost and project completion time, i.e., cost of the project can be reduced at the expense of project completion time and vice-versa.In addition, variations have been made to the genetic algorithm approach to software architecture design. To accelerate the genetic algorithm towards multi-objective solutions, a quality farms approach has been developed. The approach uses the idea of cross breeding, where different individuals that are good with respect to one quality objective are combined for producing software architecture proposals that are good in multiple objectives. Also, to explore the suitability of other methods for software architecture synthesis, a constraint satisfaction approach has been developed. The approach models the software architecture design problem as a constraint satisfaction and optimization problem and solves it using constraint satisfaction techniques. This approach can provide rationale about why certain decisions are chosen in the proposed architecture proposals.Tool support for genetic algorithm-based architecture design and work planning approaches has been proposed. It facilitates an end user to give input, view and analyze the results of the developed genetic algorithm based approaches. The tool also provides support for semi-automated architecture design, where a human architect can guide the genetic algorithm towards optimal solutions. An empirical study has also been performed. It suggests that the quality of the proposals produced through semiautomated architecture design is roughly at the level of senior software engineering students. Furthermore, the project manager can interact with the tool and perform whatif analysis for choosing the suitable work distribution for the project at hand

Energy-efficient routing algorithms based on swarm intelligence for wireless sensor networks

High efficient routing is an important factor to be considered in the design of limited energy resource Wireless Sensor Networks (WSNs). WSN environment has limited resources in terms of on-board energy, transmission power, processing, and storage, and this prompt for careful resource management and new routing protocol so as to counteract the challenges. This work first introduces the concept of wireless sensor networks, routing in WSNs, and its design factors as they affect routing protocols. Next, a comprehensive review of the most prominent routing protocols in WSN, from the classical routing protocols to swarm intelligence based protocols is presented. From the literature study, it was found that comparing routing protocols in WSNs is currently a very challenging task for protocol designers. Often, much time is required to re-create and re-simulate algorithms from descriptions in published papers to perform the comparison. Compounding the difficulty is that some simulation parameters and performance metrics may not be mentioned. We then see a need in the research community to have standard simulation and performance metrics for comparing different protocols. To this end, we re-simulate different protocols using a Matlab based simulator; Routing Modeling Application Simulation Environment (RMASE), and gives simulation results for standard simulation and performance metrics which we hope will serve as a benchmark for future comparisons for the research community. Also, from the literature study, Energy Efficient Ant-Based Routing (EEABR) protocol was found to be the most efficient protocol due to its low energy consumption and low memory usage in WSNs nodes. Following this efficient protocol, an Improved Energy Efficient Ant-Based Routing (IEEABR) Protocol was proposed. Simulation were performed using Network Simulator-2 (NS-2), and from the results, our proposed algorithm performs better in terms of energy utilization efficiency, average energy of network nodes, and minimum energy of nodes. We further improved on the proposed protocol and simulation performed in another well-known WSNs MATLAB-based simulator; Routing Modeling Application Simulation Environment (RMASE), using static, mobile and dynamic scenario. Simulation results show that the proposed algorithm increases energy efficiency by up to 9% and 64% in converge-cast and target-tracking scenarios, respectively, over the original EEABR and also found to out-perform other four Ant-based routing protocols. We further show how this algorithm could be used for energy management in sensor network in the presence of energy harvesters. However, high number of control packets is generated by the IEEABR due to the proactive nature of its path establishment. As such, a new routing protocol for WSNs that has less control packets due to its on-demand (reactive) nature is proposed. This new routing protocol termed Termite-hill is borrowed from the principles behind the termite’s mode of communication. We first study the foraging principles of a termite colony and utilize the inspirational concepts to develop a distributed, simple and energy-efficient routing protocol for WSNs. We perform simulation studies to compare the behavior and performance of the Termite-hill design with an existing classical and on-demand protocol (AODV) and other Swarm Intelligence (SI) based WSN protocols in both static, dynamic and mobility scenarios of WSN. The simulation results demonstrate that Termite-hill outperforms its competitors in most of the assumed scenarios and metrics with less latency. Further studies show that the current practice in modeling and simulation of wireless sensor network (WSN) environments has been towards the development of functional WSN systems for event gathering, and optimization of the necessary performance metrics using heuristics and intuition. The evaluation and validation are mostly done using simulation approaches and practical implementations. Simulation studies, despite their wide use and merits of network systems and algorithm validation, have some drawbacks like long simulation times, and practical implementation might be cost ineffective if the system is not properly studied before the design. We therefore argue that simulation based validation and practical implementation of WSN systems and environments should be further strengthened through mathematical analysis. To conclude this work and to gain more insight on the behavior of the termite-hill routing algorithm, we developed our modeling framework for WSN topology and information extraction in a grid based and line based randomly distributed sensor network. We strengthen the work with a model of the effect of node mobility on energy consumption of Termite-hill routing algorithm as a function of event success rate and occasional change in topology. The results of our mathematical analysis were also compared with the simulation results

Efficient Learning Machines

Computer scienc